ppmvb: Bayesian ppm-function

In antiphon/ppmvb: Variational Bayes extension to the ppm-function of package spatstat

Description

Otherwise the same as ppm with method="logi" but using a Bayesian fitter.

Usage

ppmvb(Q, ...)

Arguments

...	Parameters to ppm. Method will be "logi".

Details

The variational approximation uses the package vblogistic. 'verb' and 'eps' parameters are renamed here 'verbosity' and 'epsilon' as ppm uses the former internally.

The returned object is apart from $internal$glmfit the same as that of ppm. Some of the methods (e.g. plot) wont work at the moment.

Method vbsummary provides some extra Bayesian information.

The $internal$glmfit is of class "vblogitfit" with print, marginals and plot summary.

Prior arguments:

* m0: Gaussian prior mean vector.

* S0: Gaussian prior covariance matrix.

Make sure the dimensions match, e.g. try basic ppm and look how many parameters are estimated.

Examples

library(spatstat)
x <- rStrauss(100, 0.1, 0.07)
# glm:
f0 <- ppm(x, interaction=Strauss(0.06), method="logi")
# vb-logistic, with bad prior:
f1 <- ppmvb(x, interaction=Strauss(0.06), m0=log(c(100, 0.3)), S0=diag(c(10, 3)), verbose=TRUE )

print( exp( rbind(coef(f0), coef(f1))) )

summary(f1)



s <- vbsummary(f1)
# this is of class vblogitfit
print(s)
plot(s)
# in exp scale
plot(s, log=FALSE)

antiphon/ppmvb documentation built on May 10, 2019, 12:20 p.m.